code: def track_time(graph_name, feat_dim, aggr_type):
      device = utils.get_bench_device()
      graph = utils.get_graph(graph_name).to(device)
  
      feat = torch.randn((graph.num_nodes(), feat_dim), device=device)
      model = SAGEConv(
          feat_dim, feat_dim, aggr_type, activation=F.relu, bias=False
      ).to(device)
  
      # dry run
      for i in range(3):
          model(graph, feat)
      # timing
      with utils.Timer() as t:
          for i in range(50):
              model(graph, feat)
  
      return t.elapsed_secs / 50
  
file_path: dgl/benchmarks/benchmarks/api/bench_nn_graphconv.py
imports: import time
  import dgl
  import dgl.function
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from dgl.nn.pytorch import SAGEConv
  from ..None import utils
label_desc: Use image node_id 1 for calling a global function with example usage: track_time(graph_name, feat_dim, aggr_type) and returns: unknown